Biometric based false input detection for a wearable computing device

ABSTRACT

A wearable computing device includes various biometric sensors for recording biometric measurements of a user wearing the wearable computing device. The wearable computing device is further configured to interpret the biometric measurements as commands to be performed. The wearable computing device also includes modules and logic that determine whether the biometric measurements were voluntary or involuntary movements by the user, which indicate whether the user intended such biometric measurements to be input to the wearable computing device. Where the biometric measurements indicate that the user&#39;s movements and/or gestures were voluntary, the wearable computing device is configured to further classify and analyze the biometric measurements. This classification and analysis yields the different types of actions and objects the user was engaged in or acting on at the time the biometric measurements were obtained.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. patentapplication Ser. No. 62/339,748, titled “BIOMETRIC BASED FALSE INPUTDETECTION FOR A WEABLE COMPUTING DEVICE and filed May 20, 2016,” thedisclosure of which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to biometric basedfalse input detection for a wearable computing device and, inparticular, to determining whether a biometric based input provided viaa wearable computing device should be executed given the environment inwhich the wearable computing device is located.

BACKGROUND

Augmented reality (AR) is a live direct or indirect view of a physical,real-world environment whose elements are augmented (or supplemented) bycomputer-generated sensory input such as sound, video, graphics orGlobal Positioning System (GPS) data. With the help of advanced ARtechnology (e.g., adding computer vision and object recognition) theinformation about the surrounding real world of the user becomesinteractive. Device-generated (e.g., artificial) information about theenvironment and its objects can be overlaid on the real world.

Typically, a user uses a computing device to view the augmented reality.The computing device may be a wearable computing device used in avariety of environments. These environments may be noisy or have activeobjects and/or elements (e.g., cars, other people, advertisingbillboards, movie marquees, etc.) that draw the user's attention awayfrom the computing device. Accordingly, when the user interacts withthese active objects or elements, the computing device will typicallyinterpret the user's interaction as a command. This response by thecomputing device often causes the computing device to perform a commandand/or action that was unintended by the user. Thus, these conventionalcomputing devices that leverage augmented reality can have difficultieswhen used in environments that have different objects and/or elementsthat demands the user's attention.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limited tothe figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating an example of a networkenvironment suitable for a wearable computing device, according to anexample embodiment.

FIG. 2 is a block diagram of the wearable computing device of FIG. 1,according to an example embodiment

FIG. 3 is a block diagram illustrating different types of sensors usedby the wearable computing device of FIG. 1, according to an exampleembodiment.

FIGS. 4A-4B illustrate a method, according to an example embodiment,implemented by the wearable computing device of FIG. 1 for determiningwhether a given input is a false input.

FIG. 5 illustrates a user input classifying tree, according to anexample embodiment, used by the wearable computing device of FIG. 1 indetermining a biometric input score for a given user input.

FIG. 6 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The disclosure provides for a wearable computing device that interpretswhether a given input by the user of the wearable computing device is acommand to be performed or a false input resulting from the userinteracting with his or her environment. In one embodiment, the wearablecomputing device leverages different biometric measurements and/ormetrics to determine whether an action performed by the user is acommand or false input. For example, the wearable computing device mayleverage a detected readiness potential in combination with otherbiometric measurements (e.g., eye tracking measurements, physicalmovements by the user, etc.). As known to one of ordinary skill in theart, readiness potential is a measure of activity in the motor cortexand supplementary motor area of the brain leading up to voluntary musclemovement. A user's readiness potential can be measured by one or morebiometric sensors affixed to the wearable computing device, which canindicate whether a movement or action performed by the user wasinvoluntary or voluntary. Where the movement or action is determined asvoluntary, the readiness potential measurement can be combined withother biometric measurements to further refine the action or movement asan irrelevant or relevant action or movement. As discussed below withreference to FIG. 5, a matrix or array of different types of biometricmeasurements can be used to further refine the action or movement. Inthis manner, the disclosed wearable computing device is configured todistinguish between voluntary and involuntary actions and, where theaction is voluntary, whether the action is intended as a command to beperformed by the wearable computing device.

Accordingly, in one embodiment, the disclosed wearable computing deviceincludes a machine-readable memory storing computer-executableinstructions and at least one hardware processor in communication withthe machine-readable memory that, when the computer-executableinstructions are executed, configures the wearable computing device toperform a plurality of operations. The plurality of operations includesdetermining an environment type for an environment in which the wearablecomputing device is located, the environment type associated with anenvironment risk value, obtaining a plurality of biometric measurementsfrom one or more biometric sensors communicatively coupled to thewearable computing device, and classifying the plurality biometricmeasurements into a plurality of categories, at least one categorydefining a characteristic of the obtained biometric measurements. Theplurality of operations also includes determining a biometric inputscore based on the at least one category, comparing the determinedbiometric input score with the associated environment risk value, and inresponse to the comparison of the determined biometric input score andthe associated environment risk value, identifying the plurality ofbiometric measurements as a false input to the wearable computingdevice.

In another embodiment of the wearable computing device, the plurality ofoperations further includes determining a readiness potential from theplurality of biometric measurements, comparing the determined readinesspotential with a previously measured baseline readiness potential, andin response to the comparison of the determined readiness potential withthe previously measured baseline readiness potential, determining thatthe plurality of biometric measurements were from voluntary movements ofa user of the wearable computing device.

In a further embodiment of the wearable computing device, theenvironment type is determined by cross-referencing Global PositioningSystem (GPS) coordinates with a location associated with the environmenttype.

In yet another embodiment of the wearable computing device, determiningthe environment type includes activating at least one external camera todetect a plurality of objects, activating at least one audio sensor todetect one or more sounds, comparing the detected plurality of objectswith at least one object threshold and the detected one or more soundswith at least one noise threshold, and in response to the comparisonswith the at least one object threshold and the at least one noisethreshold, determining the environment type.

In yet a further embodiment of the wearable computing device, eachcategory of the plurality of categories is associated with aclassification value, and the biometric input score is determined fromthe classification values associated with the plurality of categories.

In another embodiment of the wearable computing device, the plurality ofoperations further include determining whether the plurality ofbiometric measurements were the result of voluntary or involuntarymovements by a user of the wearable computing device, in response to adetermination that the plurality of biometric measurements were theresult of voluntary movements, performing the classifying of theplurality biometric measurements into the plurality of categories.

In a further embodiment of the wearable computing device, the pluralityof operations further includes identifying the plurality of biometricmeasurements as an input to be interpreted as a command performable bythe wearable computing device in response to the comparison of thedetermined biometric input score and the associated environment riskvalue.

This disclosure also provides for a method for providing augmentedreality images of an environment in which a wearable computing device isworn, the method comprising determining, by at least one hardwareprocessor, an environment type for an environment in which the wearablecomputing device is located, the environment type associated with anenvironment risk value, obtaining, by one or more biometric sensorscommunicatively coupled to the wearable computing device, a plurality ofbiometric measurements, and classifying the plurality biometricmeasurements into a plurality of categories, at least one categorydefining a characteristic of the obtained biometric measurements. Themethod also includes determining a biometric input score based on the atleast one category, comparing the determined biometric input score withthe associated environment risk value, and in response to the comparisonof the determined biometric input score and the associated environmentrisk value, identifying the plurality of biometric measurements as afalse input to the wearable computing device.

In another embodiment of the method, the method includes determining areadiness potential from the plurality of biometric measurements,comparing the determined readiness potential with a previously measuredbaseline readiness potential, and in response to the comparison of thedetermined readiness potential with the previously measured baselinereadiness potential, determining that the plurality of biometricmeasurements were from voluntary movements of a user of the wearablecomputing device.

In a further embodiment of the method, the environment type isdetermined by cross-referencing Global Positioning System (GPS)coordinates with a location associated with the environment type.

In yet another embodiment of the method, determining the environmenttype includes activating at least one external camera to detect aplurality of objects, activating at least one audio sensor to detect oneor more sounds, comparing the detected plurality of objects with atleast one object threshold and the detected one or more sounds with atleast one noise threshold, and in response to the comparisons with theat least one object threshold and the at least one noise threshold,determining the environment type.

In yet a further embodiment of the method, each category of theplurality of categories is associated with a classification value, andthe biometric input score is determined from the classification valuesassociated with the plurality of categories.

In another embodiment of the method, the method includes determiningwhether the plurality of biometric measurements were the result ofvoluntary or involuntary movements by a user of the wearable computingdevice, and, in response to a determination that the plurality ofbiometric measurements were the result of voluntary movements,performing the classifying of the plurality biometric measurements intothe plurality of categories.

In a further embodiment of the method, the method includes identifyingthe plurality of biometric measurements as an input to be interpreted asa command performable by the wearable computing device in response tothe comparison of the determined biometric input score and theassociated environment risk value.

This disclosure further provides for a machine-readable memory storingcomputer-executable instructions that, when executed by at least onehardware processor in communication with the machine-readable memory,configures a wearable computing device to perform a plurality ofoperations. The plurality of operations includes determining anenvironment type for an environment in which the wearable computingdevice is located, the environment type associated with an environmentrisk value, obtaining a plurality of biometric measurements from one ormore biometric sensors communicatively coupled to the wearable computingdevice, and classifying the plurality biometric measurements into aplurality of categories, at least one category defining a characteristicof the obtained biometric measurements. The plurality of operations alsoincludes determining a biometric input score based on the at least onecategory, comparing the determined biometric input score with theassociated environment risk value, and, in response to the comparison ofthe determined biometric input score and the associated environment riskvalue, identifying the plurality of biometric measurements as a falseinput to the wearable computing device.

In another embodiment of the machine-readable memory, the plurality ofoperations further includes determining a readiness potential from theplurality of biometric measurements, comparing the determined readinesspotential with a previously measured baseline readiness potential, and,in response to the comparison of the determined readiness potential withthe previously measured baseline readiness potential, determining thatthe plurality of biometric measurements were from voluntary movements ofa user of the wearable computing device.

In a further embodiment of the machine-readable memory, the environmenttype is determined by cross-referencing Global Positioning System (GPS)coordinates with a location associated with the environment type.

In yet another embodiment of the machine-readable memory, determiningthe environment type includes activating at least one external camera todetect a plurality of objects, activating at least one audio sensor todetect one or more sounds, comparing the detected plurality of objectswith at least one object threshold and the detected one or more soundswith at least one noise threshold, and, in response to the comparisonswith the at least one object threshold and the at least one noisethreshold, determining the environment type.

In yet a further embodiment of the machine-readable memory, eachcategory of the plurality of categories is associated with aclassification value, and the biometric input score is determined fromthe classification values associated with the plurality of categories.

In another embodiment of the machine-readable memory, the plurality ofoperations further includes determining whether the plurality ofbiometric measurements were the result of voluntary or involuntarymovements by a user of the wearable computing device, and, in responseto a determination that the plurality of biometric measurements were theresult of voluntary movements, performing the classifying of theplurality biometric measurements into the plurality of categories.

This disclosure provides for another a method for providing augmentedreality images of an environment in which a wearable computing device isworn, the method including obtaining, by one or more biometric sensorscommunicatively coupled to a wearable computing device, a plurality ofbiometric measurements for a user of the wearable computing device,determining a readiness potential from the plurality of biometricmeasurements, comparing the determined readiness potential with apreviously measured baseline readiness potential, in response to thecomparison of the determined readiness potential with the previouslymeasured baseline readiness potential, determining that the plurality ofbiometric measurements were from voluntary movements of a user of thewearable computing device, and, in response to the determination thatthe plurality of biometric measurements were from voluntary movements ofthe user, interpreting the plurality of biometric measurements as acommand to be performed by the wearable computing device.

Unless explicitly stated otherwise, components and functions areoptional and may be combined or subdivided, and operations may vary insequence or be combined or subdivided. In the following description, forpurposes of explanation, numerous specific details are set forth toprovide a thorough understanding of example embodiments. It will beevident to one skilled in the art, however, that the present subjectmatter may be practiced without these specific details.

FIG. 1 is a block diagram illustrating an example of a networkenvironment 102 suitable for a wearable computing device 104, accordingto an example embodiment. The network environment 102 includes thewearable computing device 104 and a server 112 communicatively coupledto each other via a network 110. The wearable computing device 104 andthe server 112 may each be implemented in a computer system, in whole orin part, as described below with respect to FIG. 5.

The server 112 may be part of a network-based system. For example, thenetwork-based system may be or include a cloud-based server system thatprovides additional information, such as three-dimensional (3D) modelsor other virtual objects, to the wearable computing device 104.

The wearable computing device 104 may be implemented in various formfactors. In one embodiment, the wearable computing device 104 isimplemented as a helmet, which the user 120 wears on his or her head,and views objects (e.g., physical object(s) 106) through a displaydevice, such as one or more lenses, affixed to the wearable computingdevice 104. In another embodiment, the wearable computing device 104 isimplemented as a lens frame, where the display device is implemented asone or more lenses affixed thereto. In yet another embodiment, thewearable computing device 104 is implemented as a watch (e.g., a housingmounted or affixed to a wrist band), and the display device isimplemented as a display (e.g., liquid crystal display (LCD) or lightemitting diode (LED) display) affixed to the wearable computing device104.

A user 120 may wear the wearable computing device 104 and view one ormore physical object(s) 106 in a real world physical environment. Theuser 120 may be a human user (e.g., a human being), a machine user(e.g., a computer configured by a software program to interact with thewearable computing device 104), or any suitable combination thereof(e.g., a human assisted by a machine or a machine supervised by ahuman). The user 120 is not part of the network environment 102, but isassociated with the wearable computing device 104. For example, thewearable computing device 104 may be a computing device with a cameraand a transparent display. In another example embodiment, the wearablecomputing device 104 may be hand-held or may be removably mounted to thehead of the user 120. In one example, the display device may include ascreen that displays what is captured with a camera of the wearablecomputing device 104. In another example, the display may be transparentor semi-transparent, such as lenses of wearable computing glasses or thevisor or a face shield of a helmet.

The user 120 may be a user of an augmented reality (AR) applicationexecutable by the wearable computing device 104 and/or the server 112.The AR application may provide the user 120 with an AR experiencetriggered by one or more identified objects (e.g., physical object(s)106) in the physical environment. For example, the physical object(s)106 may include identifiable objects such as a two-dimensional (2D)physical object (e.g., a picture), a 3D physical object (e.g., a factorymachine), a location (e.g., at the bottom floor of a factory), or anyreferences (e.g., perceived corners of walls or furniture) in thereal-world physical environment. The AR application may include computervision recognition to determine various features within the physicalenvironment such as corners, objects, lines, letters, and other suchfeatures or combination of features.

In one embodiment, the objects in an image captured by the wearablecomputing device 104 are tracked and locally recognized using a localcontext recognition dataset or any other previously stored dataset ofthe AR application. The local context recognition dataset may include alibrary of virtual objects associated with real-world physical objectsor references. In one embodiment, the wearable computing device 104identifies feature points in an image of the physical object 106. Thewearable computing device 104 may also identify tracking data related tothe physical object 106 (e.g., GPS location of the wearable computingdevice 104, orientation, or distance to the physical object(s) 106). Ifthe captured image is not recognized locally by the wearable computingdevice 104, the wearable computing device 104 can download additionalinformation (e.g., 3D model or other augmented data) corresponding tothe captured image, from a database of the server 112 over the network110.

In another example embodiment, the physical object(s) 106 in the imageis tracked and recognized remotely by the server 112 using a remotecontext recognition dataset or any other previously stored dataset of anAR application in the server 112. The remote context recognition datasetmay include a library of virtual objects or augmented informationassociated with real-world physical objects or references.

The network environment 102 also includes one or more external sensors108 that interact with the wearable computing device 104 and/or theserver 112. The external sensors 108 may be associated with, coupled to,or related to the physical object(s) 106 to measure a location, status,and characteristics of the physical object(s) 106. Examples of measuredreadings may include but are not limited to weight, pressure,temperature, velocity, direction, position, intrinsic and extrinsicproperties, acceleration, and dimensions. For example, external sensors108 may be disposed throughout a factory floor to measure movement,pressure, orientation, and temperature. The external sensor(s) 108 canalso be used to measure a location, status, and characteristics of thewearable computing device 104 and the user 120. The server 112 cancompute readings from data generated by the external sensor(s) 108. Theserver 112 can generate virtual indicators such as vectors or colorsbased on data from external sensor(s) 108. Virtual indicators are thenoverlaid on top of a live image or a view of the physical object(s) 106(e.g., displayed on the display device 114) in a line of sight of theuser 120 to show data related to the physical object(s) 106. Forexample, the virtual indicators may include arrows with shapes andcolors that change based on real-time data. Additionally and/oralternatively, the virtual indicators are rendered at the server 112 andstreamed to the wearable computing device 104.

The external sensor(s) 108 may include one or more sensors used to trackvarious characteristics of the wearable computing device 104 including,but not limited to, the location, movement, and orientation of thewearable computing device 104 externally without having to rely onsensors internal to the wearable computing device 104. The externalsenor(s) 108 may include optical sensors (e.g., a depth-enabled 3Dcamera), wireless sensors (e.g., Bluetooth, Wi-Fi), Global PositioningSystem (GPS) sensors, and audio sensors to determine the location of theuser 120 wearing the wearable computing device 104, distance of the user120 to the external sensor(s) 108 (e.g., sensors placed in corners of avenue or a room), the orientation of the wearable computing device 104to track what the user 120 is looking at (e.g., direction at which adesignated portion of the wearable computing device 104 is pointed,e.g., the front portion of the wearable computing device 104 is pointedtowards a player on a tennis court).

Furthermore, data from the external senor(s) 108 and internal sensors(not shown) in the wearable computing device 104 may be used foranalytics data processing at the server 112 (or another server) foranalysis on usage and how the user 120 is interacting with the physicalobject(s) 106 in the physical environment. Live data from other serversmay also be used in the analytics data processing. For example, theanalytics data may track at what locations (e.g., points or features) onthe physical object(s) 106 or virtual object(s) (not shown) the user 120has looked, how long the user 120 has looked at each location on thephysical object(s) 106 or virtual object(s), how the user 120 wore thewearable computing device 104 when looking at the physical object(s) 106or virtual object(s), which features of the virtual object(s) the user120 interacted with (e.g., such as whether the user 120 engaged with thevirtual object), and any suitable combination thereof. To enhance theinteractivity with the physical object(s) 106 and/or virtual objects,the wearable computing device 104 receives a visualization contentdataset related to the analytics data. The wearable computing device104, via the display device 114, then generates a virtual object withadditional or visualization features, or a new experience, based on thevisualization content dataset.

Any of the machines, databases, or devices shown in FIG. 1 may beimplemented in a general-purpose computer modified (e.g., configured orprogrammed) by software to be a special-purpose computer to perform oneor more of the functions described herein for that machine, database, ordevice. For example, a computer system able to implement any one or moreof the methodologies described herein is discussed below with respect toFIG. 5. As used herein, a “database” is a data storage resource and maystore data structured as a text file, a table, a spreadsheet, arelational database (e.g., an object-relational database), a triplestore, a hierarchical data store, or any suitable combination thereof.Moreover, any two or more of the machines, databases, or devicesillustrated in FIG. 1 may be combined into a single machine, and thefunctions described herein for any single machine, database, or devicemay be subdivided among multiple machines, databases, or devices.

The network 108 may be any network that facilitates communicationbetween or among machines (e.g., server 110), databases, and devices(e.g., the wearable computing device 104 and the external sensor(s)108). Accordingly, the network 108 may be a wired network, a wirelessnetwork (e.g., a mobile or cellular network), or any suitablecombination thereof. The network 108 may include one or more portionsthat constitute a private network, a public network (e.g., theInternet), or any suitable combination thereof.

FIG. 2 is a block diagram of the wearable computing device 104 of FIG.1, according to an example embodiment. The wearable computing device 104includes various different types of hardware components. In oneembodiment, the wearable computing device includes one or moreprocessor(s) 202, a display 204, a communication interface 204, and oneor more sensors 208. The wearable computing device 104 also includes amachine-readable memory 210. The various components 202-210 communicatevia a communication bus 234.

The one or more processors 202 may be any type of commercially availableprocessor, such as processors available from the Intel Corporation,Advanced Micro Devices, Qualcomm, Texas Instruments, or other suchprocessors. Further still, the one or more processors 202 may includeone or more special-purpose processors, such as a Field-ProgrammableGate Array (FPGA) or an Application Specific Integrated Circuit (ASIC).The one or more processors 202 may also include programmable logic orcircuitry that is temporarily configured by software to perform certainoperations. Thus, once configured by such software, the one or moreprocessors 202 become specific machines (or specific components of amachine) uniquely tailored to perform the configured functions and areno longer general-purpose processors.

The display 204 may include a display surface or lens configured todisplay AR content (e.g., images, video) generated by the one or moreprocessor(s) 202. In one embodiment, the display 204 is made of atransparent material (e.g., glass, plastic, acrylic, etc.) so that theuser 120 can see through the display 204. In another embodiment, thedisplay 204 is made of several layers of a transparent material, whichcreates a diffraction grating within the display 204 such that imagesdisplayed on the display 204 appear holographic. The processor(s) 202are configured to display a user interface on the display 204 so thatthe user 120 can interact with the wearable computing device 104.

The communication interface 206 is configured to facilitatecommunications between the wearable computing device 104, the user 120and the external sensor(s) 108. The communication interface 206 mayinclude one or more wired communication interfaces (e.g., UniversalSerial Bus (USB), an I²C bus, an RS-232 interface, an RS-485 interface,etc.), one or more wireless transceivers, such as a Bluetooth®transceiver, a Near Field Communication (NFC) transceiver, an 802.11xtransceiver, a 3G (e.g., a GSM and/or CDMA) transceiver, a 4G (e.g., LTEand/or Mobile WiMAX) transceiver, or combinations of wired and wirelessinterfaces and transceivers. In one embodiment, the communicationinterface 206 interacts with the sensors 208 to provide input to thewearable computing device 104. In this embodiment, the user 120 mayengage in gestures, eye movements, speech, or other physical activitiesthat the wearable computing device 104 interprets as input (e.g., viathe AR application 214 and/or input detection module 218).

However, every movement and/or gesture performed by the user 120 may notnecessarily be an input for the wearable computing device 104. Asdiscussed below with reference to FIGS. 4A-4B, the wearable computingdevice 104 is configured to determine whether such movements and/orgestures are, in fact, inputs corresponding to a command that thewearable computing device 104 is to perform.

To detect the movements of the user 120, the wearable computing device104, and/or other objects in the environment, the wearable computingdevice 104 includes one or more sensors 208. The sensors 208 maygenerate internal tracking data of the wearable computing device 104 todetermine a position and/or an orientation of the wearable computingdevice 104. In addition, the sensors 208 cooperatively operate so as toassist the wearable computing device 104 in determining whether anaction performed by the user 120 was intended to be a commandperformable by the wearable computing device 104.

The position and the orientation of the wearable computing device 104may be used to identify real-world objects in a field of view of thewearable computing device 104. For example, a virtual object may berendered and displayed in the display 204 when the sensors 208 indicatethat the wearable computing device 104 is oriented towards a real-worldobject (e.g., when the user 120 looks at one or more physical object(s)106) or in a particular direction (e.g., when the user 120 tilts hishead to watch his wrist).

In addition, the position and/or orientation of the wearable computingdevice 104 may signal whether the user 120 intends a given physiologicalaction (e.g., a gesture, eye movement, eye gaze, etc.) to be a commandfor the wearable computing device 104. For example, the

The wearable computing device 104 may display a virtual object inresponse to a determined geographic location of the wearable computingdevice 104. For example, a set of virtual objects may be accessible whenthe user 120 of the wearable computing device 104 is located in aparticular building. In another example, virtual objects, includingsensitive material, may be accessible when the user 120 of the wearablecomputing device 104 is located within a predefined area associated withthe sensitive material and the user 120 is authenticated. Differentlevels of content of the virtual objects may be accessible based on acredential level of the user 120. For example, a user who is anexecutive of a company may have access to more information or content inthe virtual objects than a manager at the same company. The sensors 208may be used to authenticate the user 120 prior to providing the user 120with access to the sensitive material (e.g., information displayed in asa virtual object such as a virtual dialog box in a transparent display).Authentication may be achieved via a variety of methods such asproviding a password or an authentication token, or using sensors 208 todetermine biometric data unique to the user 120.

FIG. 3 is a block diagram illustrating different types of sensors 208used by the wearable computing device 104 of FIG. 1, according to anexample embodiment. For example, the sensors 208 may include an externalcamera 302, an inertial measurement unit (IMU) 304, a location sensor306, an audio sensor 308, an ambient light sensor 310, and one or morebiometric sensors 312. One of ordinary skill in the art will appreciatethat the sensors illustrated in FIG. 3 are examples, and that differenttypes and/or combinations of sensors may be employed in the wearablecomputing device 104.

The external camera 302 includes an optical sensor(s) (e.g., camera)configured to capture images across various spectrums. For example, theexternal camera 302 may include an infrared camera or a full-spectrumcamera. The external camera 302 may include a rear-facing camera(s) anda front-facing camera(s) disposed in the wearable computing device 104.The front-facing camera(s) may be used to capture a front field of viewof the wearable computing device 104 while the rear-facing camera(s) maybe used to capture a rear field of view of the wearable computing device104. The pictures captured with the front- and rear-facing cameras maybe combined to recreate a 360-degree view of the physical environmentaround the wearable computing device 104.

The IMU 304 may include a gyroscope and an inertial motion sensor todetermine an orientation and/or movement of the wearable computingdevice 104. For example, the IMU 304 may measure the velocity,orientation, and gravitational forces on the wearable computing device104. The IMU 304 may also measure acceleration using an accelerometerand changes in angular rotation using a gyroscope.

The location sensor 306 may determine a geolocation of the wearablecomputing device 104 using a variety of techniques such as near fieldcommunication (NFC), the Global Positioning System (GPS), Bluetooth®,Wi-Fi®, and other such wireless technologies or combination of wirelesstechnologies. For example, the location sensor 306 may generategeographic coordinates and/or an elevation of the wearable computingdevice 104.

The audio sensor 308 may include one or more sensors configured todetect sound, such as a dynamic microphone, condenser microphone, ribbonmicrophone, carbon microphone, and other such sound sensors orcombinations thereof. For example, the microphone may be used to recorda voice command from the user (e.g., user 120) of the wearable computingdevice 104. In other examples, the microphone may be used to measure anambient noise (e.g., measure intensity of the background noise, identifyspecific type of noises such as explosions or gunshot noises).

The ambient light sensor 310 is configured to determine an ambient lightintensity around the wearable computing device 104. For example, theambient light sensor 314 measures the ambient light in a room in whichthe wearable computing device 104 is located. Examples of the ambientlight sensor 310 include, but are not limited to, the ambient lightsensors available from ams AG, located in Oberpremstätten, Austria.

The biometric sensors 312 include sensors configured to measurebiometric data of the user 120 of the wearable computing device 104. Inone example embodiment, the biometric sensors 312 include an ocularcamera 314, one or more electroencephalogram (EEG) sensors 316, and oneor more electrocardiogram (ECG) sensors 318. One of ordinary skill inthe art will appreciate that the biometric sensors 312 illustrated inFIG. 3 are examples, and that different types and/or combinations ofbiometric sensors 312 may be employed in the wearable computing device104.

The biometric sensors 312 may be affixed to different parts and/orsurfaces of the wearable computing device 104 depending upon itsimplementation. For example, where the wearable computing device 104 isimplemented as a head-mounted device, one or more of the biometricsensors 312 may be disposed within an interior surface of the wearablecomputing device 104 such that the one or more biometric sensors 312 arein contact with the skin of the user's 120 head (e.g., the forehead). Asanother example, where the wearable computing device 104 is implementedas a wrist-mounted device (e.g., a watch), one or more of the biometricsensors 312 may be disposed within, or in contact with, an exteriorsurface of the wearable computing device 104 such that the one or morebiometric sensors 312 are also in contact with the skin of one of theuser's 120 limbs (e.g., a wrist of a forearm). In either examples, theone or more biometric sensors 312 are arranged or disposed within thewearable computing device 104 such that it records physiological signalsfrom the user 120.

One example of a biometric sensor 312 is an ocular camera 314. Theocular camera 314 may include an infrared (IR) camera configured tocapture an image of a retina of the user 120. The IR camera may be usedto perform a retinal scan to map unique patterns of the retina of theuser 120. Blood vessels within the retina absorb light more readily thanthe surrounding tissue in the retina and therefore can be identifiedwith IR lighting. The IR camera may cast a beam of IR light into theuser's eye as the user 120 looks through the display 204 (e.g., lenses)towards virtual objects rendered in the display 204. The beam of IRlight traces a path on the retina of the user 120. Because retinal bloodvessels absorb more of the IR light than the rest of the eye, the amountof reflection varies during the retinal scan. The pattern of variationsmay be used as a biometric data unique to the user 120.

Alternatively and/or additionally, the ocular camera 314 may be a cameraconfigured to capture an image of an iris in the eye of the user 120. Inresponse to the amount of light entering the eye, muscles attached tothe iris expand or contract the aperture at the center of the iris,known as the pupil. The expansion and contraction of the pupil dependson the amount of ambient light. The ocular camera 314 may use irisrecognition as a method for biometric identification. The complexpattern on the iris of the eye of the user 120 is unique and can be usedto identify the user 120. The ocular camera 314 may cast infrared lightto acquire images of detailed structures of the iris of the eye of theuser 120. Biometric algorithms may be applied to the image of thedetailed structures of the iris to identify the user 120.

In another example embodiment, the ocular camera 306 includes an IRpupil dimension sensor that is pointed at an eye of the user 102 tomeasure the size of the pupil of the user 102. The IR pupil dimensionsensor may sample the size of the pupil (e.g., using an IR camera) on aperiodic basis or based on predefined triggered events (e.g., the user102 walks into a different room, sudden changes in the ambient light, orthe like).

Further still, the ocular camera 314 may be configured to determine thedirection in which the eye of the user 120 is looking. This eye trackingfeature may facilitate the user's interaction with a graphical userinterface displayed on the display 204. In one example embodiment, theocular camera 314 implements one or more eye tracking products, such asthe eye tracking products available from SensoMotoric Instruments GmBH,which is located in Teltow, Germany

The one or more EEG sensor(s) 316 are configured to measure electricalactivity of the brain of the user 120. In one embodiment, the one ormore EEG sensor(s) 316 are affixed to one or more surfaces of thewearable computing device 104, such that the one or more EEG sensor(s)316 come into contact with the skin of the user 120 when the wearablecomputing device 104 is worn. The one or more EEG sensor(s) 316 may alsomeasure the electrical activity and wave patterns through differentbands of frequency (e.g., Delta, Theta, Alpha, Beta, Gamma, Mu).

The one or more EEG sensor(s) 316 are further configured to provide anoutput of the measured electrical activity of the user 120, which mayindicate the user's readiness potential and/or one or more event-relatedpotentials. In one embodiment, the wearable computing device 104 iscalibrated to the electrical activity of the user 120, such that one ormore baseline measurements are established that define the user'sreadiness potential. The baseline measurements may also define variousevent-related potentials specific to the user 120. Further still, thesebaseline measurements may be established for a variety of circumstancesin which the user 120 is engaged with the wearable computing device 104or while the user 120 is interacting with his or her environment whilethe wearable computing device 104 is being worn. Additionally and/oralternatively, the baseline measurements may include one or more defaultmeasurements derived from empirical research. By establishing suchbaseline measurements, the wearable computing device 104 can beconfigured to distinguish between voluntary and involuntary movements,as discussed in Keller, et al., Readiness Potentials PrecedingSpontaneous Motor Acts: Voluntary vs. Involuntary Control, 76Electroencephalography and Clinical Neurophysiology 351 (1990), which isincorporated by reference in its entirety. Additional techniques formeasuring and identifying readiness potential is also explained inBenjamin Libet, Unconscious Cerebral Initiative and the Role ofConscious Will in Voluntary Action, 8 The Behavioral and Brain Sciences529 (1985), and Libet et al., Time of Conscious Intention to Act inRelation to Onset of Cerebral Activity (Readiness-Potential): TheUnconscious Initiation of a Freely Voluntary Act, 106 Brain 623 (1983),both of which are also incorporated by reference in their entirety.

These baseline measurements may be stored at the server 112 (e.g., thewearable computing device 104 communicates such measurements to theserver 112 via the network 110), on the wearable computing device 104itself (e.g., by being stored in the machine-readable memory 206), or acombination of both local storage and remote storage. During operationof the wearable computing device 104, the measured electrical activityof the user 120 may be compared with the one or more previously storedbaseline measurements to determine whether the measured electricalactivity corresponds to one or more of the baseline measurements and/orone or more event-related potentials. Should the wearable computingdevice 104 determine that the measured electrical activity correspondsto one or more of the baseline measurements (e.g., that the user's 120readiness potential has been detected), the wearable computing device104 then analyzes other biometric measurements to determine what type ofaction the user 120 has performed.

The one or more ECG sensor(s) 318 are configured to measure the heartrate of the user 120. In one embodiment, the one or more ECG sensor(s)310 include one or more electrodes that measure the cardiac rhythm ofthe user 120. In addition, the one or more EEG sensor(s) 316 and the oneor more ECG sensor(s) 318 may be combined into a same set of electrodesto measure both brain electrical activity and heart rate. The set ofelectrodes may be disposed one or more surfaces of the wearablecomputing device 104 so that the set of electrodes comes into contactwith the skin of the user 120 when the user 120 wears the wearablecomputing device 104.

Referring back to FIG. 2, the machine-readable memory 210 includesvarious modules 212 and data 214 for implementing the features of thewearable computing device 104. The machine-readable memory 210 includesone or more devices configured to store instructions and datatemporarily or permanently and may include, but not be limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/orany suitable combination thereof. The term “machine-readable memory”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store the modules 212 and the data 214. Accordingly, themachine-readable memory 210 may be implemented as a single storageapparatus or device, or, alternatively and/or additionally, as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. As shown in FIG. 2, the machine-readablememory 210 excludes signals per se.

In one embodiment, the modules 212 are written in a computer-programmingand/or scripting language. Examples of such languages include, but arenot limited to, C, C++, C#, Java, JavaScript, Perl, Python, Ruby, or anyother computer programming and/or scripting language now known or laterdeveloped.

The modules 212 include one or more modules 216-220 that implement thefeatures of the wearable computing device 104. In one embodiment, themodules 212 include an AR application 216, an environment detectionmodule 218, and an input detection module 220. The data 214 includes oneor more different sets of data 222-232 used by, or in support of, themodules 212. In one embodiment, the data 214 includes AR applicationdata 222, environment data 224, input detection logic 226, biometricinput data 228, biometric intention data 230, and a biometric inputscore 232.

The AR application 216 is configured to provide the user 120 with an ARexperience triggered by one or more of the physical object(s) 106 in theuser's 120 environment. Accordingly, the machine-readable memory 210also stores AR application data 222 which provides the resources (e.g.,sounds, images, text, and other such audiovisual content) used by the ARapplication 216. In response to detecting and/or identifying physicalobject(s) 106 in the user's 120 environment, the AR application 216generates audiovisual content (e.g., represented by the AR applicationdata 222) that is displayed on the display 204. To detect and/oridentify the physical object(s) 106, the AR application 216 may employvarious object recognition algorithms and/or image recognitionalgorithms.

The AR application 216 may further generate and/or display interactiveaudiovisual content on the display 204. In one embodiment, the ARapplication 214 generates an interactive graphical user interface thatthe user 120 may use to interact with the AR application 216 and/orcontrol various functions of the wearable computing device 104. Inaddition, the wearable computing device 104 may translate physicalmovements and/or gestures, performed by the user 120, as input for thegraphical user interface. However, as discussed above, some physicalmovements and/or gestures may be movements and/or gestures that the user120 does not intend to be input for the graphical user interface or theAR application 214. Such inputs may be ignored and/or cancelled by thewearable computing device 104 in response to a determination of thewhether the movement and/or action was intended to be performed and thetype of environment in which the movement and/or action was performed.

As the wearable computing device 104 is intended to be worn by the user120, the wearable computing device 104 may be worn in a variety ofenvironments. As one of ordinary skill in the art will appreciate, someenvironments are busier or nosier than other environments. For example,an empty warehouse room is less busy than a street intersection. Onewould expect that the user 120 engages in more movements/actions in abusy environment and less movements/actions in a less busy environment.In other words, it is expected that there will be more false inputs tothe wearable computing device 104 in certain environments where the user120 is expected to engage in a moderate degree of physical activity.Thus, the wearable computing device 104 is configured to account for theuser's environment in determining whether a given gesture or movement isintended to be a command to the wearable computing device 104.

In one embodiment, the wearable computing device 104 includes anenvironment detection module 218 configured to determine the type ofenvironment in which the wearable computing device 104 is located. Theenvironment detection module 218 may be configured to distinguishbetween different types of environments and assign a correspondingenvironment risk value based on the type of detected environment. In oneembodiment, the environment detection module 218 is configured todistinguish between three different types of environments: a high-riskenvironment, which is assigned an environment risk value of 20; amedium-risk environment, which is assigned an environment risk value of15; and a low-risk environment, which is assigned an environment riskvalue of 10. The environment risk value assigned to the detectedenvironment type may be stored as the environment data 224.

While the foregoing example identifies three different types ofenvironments, one of ordinary skill in the art will appreciate thatadditional or different types of environments can be defined dependingon the level of desired granularity in identifying environment types.Thus, the environment data 224 may include multiple environments types(e.g., five environment types, 10 environments types, etc.), where eachenvironment type has a corresponding environment risk value. In thismanner, the wearable computing device 104 can be configured toaccommodate many different types of environments and not just the threetypes described in the example above.

To determine the type of environment in which the wearable computingdevice 104 is located, the environment detection module 218 may leverageone or more of the sensors 208. For example, and with reference to FIG.3, the environment detection module 218 may activate the location sensor306 to determine the location of the wearable computing device 104. Inturn, the location sensor 306 may provide a set of coordinates (e.g.,GPS coordinates) to the environment detection module 218. Theenvironment detection module 218 may then reference the environment data224 with the provided set of coordinates to determine the environmenttype where the wearable computing device 104 is located. In thisembodiment, the environment data 224 may include a lookup table or otherreference data structure where the environment detection module 218 cancross reference provided sets of coordinates with the correspondingenvironment type. Additionally and/or alternatively, the environmentdetection module 218 may communicate the provided set of coordinates tothe server 112, which in turn, communicates the environment type to theenvironment detection module 218. In yet another additional and/oralternative implementation, the environment detection module 218 mayleverage one or more of the sensors 208 to identify one or more beaconswithin the environment of the wearable computing device 104, where theenvironment detection module 218 uses one or more triangulationtechniques to identify the location of the wearable computing device104. Using the determined environments type, the environment detectionmodule 218 then obtains the corresponding environment risk value.

The environment detection module 218 may also leverage one or more ofthe other sensors 208 in addition to, or alternatively from, thelocation sensor 306 and determining environment type where the wearablecomputing device 104 is located. In one embodiment, the environmentdetection module 218 activates the external camera 302 and the audiosensor 308. In this embodiment, it is assumed that busier or riskierenvironments will have more objects and/or have more noise (e.g., loudernoises and/or different types of noises). Accordingly, the environmentdata 224 includes a set of detected object thresholds corresponding tothe external camera 302 and a set of noise thresholds corresponding tothe audio sensor 308.

With regard to the detected object thresholds, a detected objectthreshold corresponds to the number of identified objects captured bythe external camera 302. Thus, a first detected object threshold maycorrespond to 10 objects, a second detected object threshold maycorrespond to 50 objects, and a third detected object threshold maycorrespond to 100 objects. In this example, the detected objectthreshold and the environment risk value have a direct relationship;thus, as the number of detected objects increases so does thecorresponding environment risk value.

The noise thresholds are configured similarly to the detected objectthresholds. In one embodiment, the noise thresholds are measured indecibels (dB) and the noise thresholds may include multiple noisethresholds to account for different types of noisy or quietenvironments. As with the detected object thresholds, the noisethresholds may also be directly related to the environment risk valuesuch that, as the sound detected by the audio sensor 308 becomes louder(e.g., an increase in the measured decibels), the environment risk valuealso increases. The amount of increase in the environment risk value maybe a predetermined amount such that the environment risk value linearlyincreases with the detected object threshold and or noise threshold orthe environment risk value may increase by other amounts, such as by anexponential or logarithmic amount.

Additionally and/or alternatively, the environment risk value may beassociated with a task being performed by the user 120 of the wearablecomputing device 104. In this manner, tasks assigned to the user 120 maybe associated with corresponding risk values, where the motions and/orgestures by the user 120 are interpreted as input to the wearablecomputing device 104 depending on the assigned task.

The wearable computing device 104 also detects the motions and/oractivities of the user 120 to determine whether such motions and/oractivities correspond to an intended input by the user 120. Accordingly,in one embodiment, the wearable computing device 104 includes an inputdetection module 220 configured to interpret and evaluate the user'smotions, gestures, and other such movements. In interpreting andevaluating these movements, the wearable computing device 104 alsoincludes input detection logic 226, which instructs the input detectionmodule 220 as to how different types of inputs are to be processed. Theinput detection logic 226 is discussed further with reference to FIG. 5below.

The input detection module 220 is also configured to determine whether agiven set of inputs (e.g., one or more motions and/or gestures) isintended as a command to the wearable computing device 104. In thisregard, input detection module 220 references the biometric input data228, which includes biometric measurements obtained by the varioussensors 208. The input detection module 220 evaluates the biometricinput data 228 according to the input detection logic 226 to classifythe retrieved biometric input data 228 into one or more categories. Asdiscussed with reference to FIG. 5, such categories include, but are notlimited to, whether the motions and/or gestures by the user 120 werevoluntary or involuntary, whether the movement and/or gesture was anautomatic or non-automatic, whether the user 120 was viewing an animateor inanimate object when the motion and/or gesture was made, and othersuch categories which are described in more detail below. The classifiedbiometric input data 228 is then stored as the biometric intention data230.

The input detection module 220 then assigns corresponding classificationvalues to the classified biometric input. The classification valuesassignable to the classified biometric input data may be predeterminedand/or programmed by an administrator or operator of the wearablecomputing device 104. In one embodiment, the classification values arestored on the wearable computing device 104 as part of the biometricintention data 230. Alternatively and/or additionally, theclassification values may be stored on the server 112, which thewearable computing device 104 retrieves via the communication interface206. The input detection module 220 then determines a biometric inputscore 232 according to the classification values assigned to theclassified biometric input data.

The biometric input score 232 serves as a basis by which the inputdetection module 220 determines whether a given gesture or motion by theuser 120 is to be interpreted as a command to the wearable computingdevice 104. In one embodiment, the biometric input score 232 is comparedwith the environment risk value previously determined by the environmentdetection module 218. For example, where the biometric input score 232is equal to or greater than the environment risk value, the inputdetection module 220 may determine that a command was not intended, andtherefore, ignores the gestures and/or movements by the user 120.Conversely, and in this example, where the biometric input score 232does not exceed the environment risk value, the input detection module220 determines that command was intended and passes the biometric inputdata 228 to the AR application 216 for interpreting the biometric inputdata 228 as an intended command. As an alternative example, where thebiometric input score 232 is equal to or greater than the environmentrisk value, the input detection module 220 may determine that commandwas intended.

FIGS. 4A-4B illustrate a method 402, according to an example embodiment,implemented by the wearable computing device 104 of FIG. 1 fordetermining whether a given input is a false input. The method may beimplemented by one or more components of the wearable computing device104 and is discussed by way of reference thereto.

Referring initially to FIG. 4A, the environment detection module 218determines an environment type for the wearable computing device 104 islocated (Operation 404). As explained above, the environment detectionmodule 218 may use one or more of the sensors 208 in determining thisenvironment type. Examples of environment types include, but are notlimited to, a high-risk environment, a medium-risk environment, and alow-risk environment. Other types of environments may also be defineddepending upon the level of granularity in determining environment typesdesired by the user and/or administrator of the wearable computingdevice 104.

In one embodiment, the environment detection module 218 determines theenvironment type using the location sensor 306, such as bycross-referencing GPS coordinates obtained by the location sensor 306with environment types stored either at the server 112 or as part of theenvironment data 224 stored by the wearable computing device 104.Additionally and/or alternatively, the environment detection module 218determines the environment type using the external camera 302 and/or theaudio sensor 308 and comparing the measurements obtained by thesesensors with preconfigured thresholds (e.g., the environment data 224).As discussed above, such thresholds may include one or more detectedobject thresholds and/or one or more noise thresholds. Each of thethresholds (e.g., the various detected object thresholds and the noisethresholds) may correspond to a defined environment type. In response tothe comparison of the various thresholds and/or the location determinedby the location sensor 306, the environment detection module 218 setsthe environment type of the environment to a “low-risk environment”(Operation 406), a “medium-risk environment” (Operation 408), or a“high-risk environment” (Operation 410).

Having determined the type of environment where the wearable computingdevice 104 is located, the input detection module 220 then detects inputfrom the user 120 (Operation 412). As explained above, the user inputmay be detected by one or more of the sensors 208 and stored as thebiometric input data 228. The input detection module 220 may thendetermine whether user input was voluntary or involuntary. Aninvoluntary user input signifies that the user did not intend to provideinput to the wearable computing device 104 that would be interpreted asa command, whereas the voluntary user input signifies that the user didintend to provide input that would be interpreted as a command.

In one embodiment, the input detection module 220 determines whether theuser provided input was voluntary or involuntary by comparing thebiometric input data 228 obtained by the EEG sensor 316 with one or morepreviously calibrated readiness potential values and/or one or morepreviously calibrated event-related potential values for the user 120(Operation 414). Where the biometric input data 228 does not approximateany one of the one or more potential measurements (e.g., the readinesspotential measurements and/or event-related potential measurements)previously obtained (e.g., within a predetermined degree of tolerance),the input detection module 220 determines that the user input was aninvoluntary input (e.g., the “INVOLUNTARY” branch of Operation 414).Where the user input is determined to be involuntary, the inputdetection module 220 ignores the provided user input (Operation 416).

In contrast, where the biometric input data 228 approximates any one ofthe one or more measurements previously obtained (e.g., within thepredetermined degree of tolerance), the input detection module 220determines that the user input was voluntary (e.g., the “VOLUNTARY”branch of Operation 414). Referring to FIG. 4B, the input detectionmodule 220 then evaluates the provided user input (e.g., the biometricinput data 228) by classifying the provided user input into one or morecategories. Each category is assigned a value, which are discussed belowwith reference to FIG. 5. As discussed above, the values of the variouscategories may be stored on the wearable computing device 104 as thebiometric intention data 230 and/or accessible by the wearable computingdevice 104 via the communication interface 206. The input detectionmodule 220 then evaluates the assigned values to determine a biometricinput score 232 (Operation 418).

The input detection module 220 uses the biometric input score 232 todetermine whether provided user input should be interpreted as a commandor ignored. In one embodiment, the biometric input score 232 is comparedwith the environment risk value corresponding to the environment typedetermined by the environment detection module 218 (Operation 420).Where the input detection module 220 determines that the biometric inputscore 232 is equal to or greater than the environment risk value (e.g.,“YES” branch of Operation 422), the input detection module 220 ignoresthe provided user input. Alternatively, where the input detection module220 determines that the biometric input is less than the environmentrisk value (e.g., “NO” branch of Operation 420), the input detectionmodule 220 determines that the user provided input should be interpretedas a command by the wearable computing device 104. Accordingly, in oneembodiment, the input detection module 220 instructs the AR application216 to determine and/or interpret a command to be performed from thebiometric input data 228 (Operation 424). The AR application 216 thenperforms the determined command (Operation 426).

FIG. 5 illustrates a user input classifying tree 502, according to anexample embodiment, used by the wearable computing device of FIG. 1 indetermining a biometric input score 232 from the biometric input data228. In one embodiment, the user input classifying tree 502 is stored aspart of the biometric intention data 230. The input detection module 220references the user input classifying tree 502 in classifying thebiometric input data 228 and assigning values to the classifiedbiometric input data.

As shown in FIG. 5, the user input classifying tree 502 includes severaldifferent categories 506-526 for classifying the user input 504 (e.g.,the biometric input data 228). In general, a category 506-526 defines acharacteristic of the biometric input data 228. In one embodiment, theinput detection module 220 classifies the biometric input data 228 usingone or more of the classification techniques as previously discussed.Once classified, the input detection module 220 assigns a classificationvalue for the corresponding category in which the biometric input data228 has been classified. Table 1, below, lists the various categoriesinto which the biometric input data 228 may be classified, a briefdescription of each category, and an example of a classification valuethat may be assigned to a corresponding category.

TABLE 1 Classification Ref. No. Category Brief Description Value 506Automatic Indicates that the 0 biometric input provided by the user wasan automatic response 508 Non-automatic Indicates that the 10 biometricinput provided by the user was intentionally controlled by the user 518Expected Indicates that the 0 biometric input was expected from the user518 Unexpected Indicates that the 10 biometric was not expected from theuser 508 Animate Indicates that the user 3 was looking at an animateobject 508 Inanimate Indicates that the user 0 was looking at aninanimate object 520 Human Indicates that the 5 object being viewed bythe user is human 520 Non-human Indicates that the 0 object viewed bythe user is not human 522 Face Indicates that the user 7 was looking ata human face 522 Object Indicates that the user 0 was looking at anobject (e.g., other than a human face) 524 Empathy Indicates that theuser 7 understands and shares the feelings of a target entity 524 NoEmpathy Indicates that the user 0 does not share with the feelings of atarget entity. 526 Relating to Self Indicates that the user 0 isintrospecting in some way (e.g., not communicating with other entities;thinking about the self, etc.) 526 Relating to Other Indicates that theuser 3 is not introspecting, but interacting with the outside world insome way. 510 Screen Indicates that the user 0 was looking at thedisplay 204 of the wearable computing device 104 510 Real-worldIndicates that the user 5 was looking at his or her environment 512 UserSpeaking Indicates that the user 0 was speaking at the time thebiometric input was received 512 Other Speaking Indicates that a person10 other than the user was speaking at the time the biometric input datawas received. 514 Focused Indicates that the 0 user's eyes were focusedon a particular object 514 Unfocused Indicates that the 15 user's eyeswere not focused on a particular object 516 Eyes On AR UI Indicates thatthe 0 user's eyes were looking at a user interface provided by the ARapplication 216 516 Eyes Off AR UI Indicates that the 3 user's eyes werelooking at an object other than the user interface provided by the ARapplication 216

Although Table 1 demonstrates that the biometric input data 228 may beclassified into 22 different categories, the input detection module 220may classify the biometric input data 228 into fewer categories orselected categories. Thus, in some instances, not every category will beused in classifying the biometric input data 228. As one of ordinaryskill in the art will appreciate, the biometric input data 228 may beclassified into a variety of categories, depending on the type ofbiometric input data received.

The input detection module 220 determines the biometric input score 232according to the classification values associated with the classifiedbiometric input data. In one embodiment, this process is additive, wherethe summation of the classification values yields the biometric inputscore 232. As one example, suppose that the user 120 waves to anotherperson while wearing the wearable computing device 104. In this example,the input detection module 220 determines that: the gesture (e.g., thewaving) is voluntary, that the user is looking at an animate object(e.g., a classification value of 3), that the animate object is human(e.g., a classification value of 5), and that the user 120 is looking ata face of the animate object (e.g., a classification value of 7). Wherethe biometric input score 232 is a summation of the classificationvalues, the input detection module 220 determines that the biometricinput score 232 for this particular interaction is 15 (e.g., 3+5+7).This determined biometric input score 232 is then compared with theenvironment risk value determined by the environment detection module218. As discussed above, depending on the results of the comparison, theinput detection module 220 ignores the provided input or allows thebiometric input data 228 to be interpreted as a command by the ARapplication 216.

In this manner, the wearable computing device 104 is configured todetermine whether given biometric inputs from a user 120 should beinterpreted as a command to be performed by the AR application 216and/or the wearable computing computer device 104, or whether suchinputs should be ignored given the environment in which the wearablecomputing device 104 is located. As some environments are inherentlymore active than others, it is understandable that the user 120 mayengage in some actions, gestures, and/or movements that are not intendedto be inputs to the wearable computing device 104. Subsequently, thisimprovement also results in better performance in the wearable computingdevice 104 as the wearable computing device 104 is prevented fromperforming otherwise undesired commands and/operations, which saves oncomputing resources available to the wearable computing device 104.Thus, the subject matter of this disclosure provides a technicalsolution to a technical problem that arises when a wearable computingdevice 104 is configured to accept commands via biometric input.

Modules, Components, and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

Example Machine Architecture and Machine-Readable Medium

FIG. 6 is a block diagram illustrating components of a machine 600,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 6 shows a diagrammatic representation of the machine600 in the example form of a computer system, within which instructions616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 600 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions may cause the machine to execute the method illustratedin FIGS. 4A-4B. Additionally, or alternatively, the instructions mayimplement one or more of the modules 212 illustrated in FIG. 2 and soforth. The instructions transform the general, non-programmed machineinto a particular machine programmed to carry out the described andillustrated functions in the manner described. In alternativeembodiments, the machine 600 operates as a standalone device or may becoupled (e.g., networked) to other machines. In a networked deployment,the machine 600 may operate in the capacity of a server machine or aclient machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine 600 may comprise, but not be limited to, a server computer, aclient computer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a set-top box (STB), a personal digital assistant(PDA), an entertainment media system, a cellular telephone, a smartphone, a mobile device, a wearable device (e.g., a smart watch), a smarthome device (e.g., a smart appliance), other smart devices, a webappliance, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 616, sequentially orotherwise, that specify actions to be taken by machine 600. Further,while only a single machine 600 is illustrated, the term “machine” shallalso be taken to include a collection of machines 600 that individuallyor jointly execute the instructions 616 to perform any one or more ofthe methodologies discussed herein.

The machine 600 may include processors 610, memory 630, and I/Ocomponents 650, which may be configured to communicate with each othersuch as via a bus 602. In an example embodiment, the processors 610(e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) processor, a Complex Instruction Set Computing (CISC)processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an Application Specific Integrated Circuit (ASIC), aRadio-Frequency Integrated Circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, processor 612and processor 614 that may execute instructions 616. The term“processor” is intended to include multi-core processor that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.6 shows multiple processors, the machine 600 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core process), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory/storage 630 may include a memory 632, such as a main memory,or other memory storage, and a storage unit 636, both accessible to theprocessors 610 such as via the bus 602. The storage unit 636 and memory632 store the instructions 616 embodying any one or more of themethodologies or functions described herein. The instructions 616 mayalso reside, completely or partially, within the memory 632, within thestorage unit 636, within at least one of the processors 610 (e.g.,within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 600. Accordingly, thememory 632, the storage unit 636, and the memory of processors 610 areexamples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot be limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)) and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store instructions 616. The term“machine-readable medium” shall also be taken to include any medium, orcombination of multiple media, that is capable of storing instructions(e.g., instructions 616) for execution by a machine (e.g., machine 600),such that the instructions, when executed by one or more processors ofthe machine 600 (e.g., processors 610), cause the machine 600 to performany one or more of the methodologies described herein. Accordingly, a“machine-readable medium” refers to a single storage apparatus ordevice, as well as “cloud-based” storage systems or storage networksthat include multiple storage apparatus or devices. The term“machine-readable medium” excludes signals per se.

The I/O components 650 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 650 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 650may include many other components that are not shown in FIG. 6. The I/Ocomponents 650 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 650 mayinclude output components 652 and input components 654. The outputcomponents 652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 650 may includebiometric components 656, motion components 658, environmentalcomponents 660, or position components 662 among a wide array of othercomponents. For example, the biometric components 656 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 658 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 660 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 662 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 650 may include communication components 664 operableto couple the machine 600 to a network 680 or devices 670 via coupling682 and coupling 672 respectively. For example, the communicationcomponents 664 may include a network interface component or othersuitable device to interface with the network 680. In further examples,communication components 664 may include wired communication components,wireless communication components, cellular communication components,Near Field Communication (NFC) components, Bluetooth® components (e.g.,Bluetooth® Low Energy), Wi-Fi® components, and other communicationcomponents to provide communication via other modalities. The devices670 may be another machine or any of a wide variety of peripheraldevices (e.g., a peripheral device coupled via a Universal Serial Bus(USB)).

Moreover, the communication components 664 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 664 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components664, such as, location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting a NFC beaconsignal that may indicate a particular location, and so forth.

Transmission Medium

In various example embodiments, one or more portions of the network 680may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, the network 680 or a portion of the network 680may include a wireless or cellular network and the coupling 682 may be aCode Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or other type of cellular orwireless coupling. In this example, the coupling 682 may implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard setting organizations, other long rangeprotocols, or other data transfer technology.

The instructions 616 may be transmitted or received over the network 680using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components664) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions616 may be transmitted or received using a transmission medium via thecoupling 672 (e.g., a peer-to-peer coupling) to devices 670. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying instructions 616 forexecution by the machine 600, and includes digital or analogcommunications signals or other intangible medium to facilitatecommunication of such software.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

We claim:
 1. A wearable computing device for providing augmented realityimages of an environment in which the wearable computing device is worn,the wearable computing device comprising: a machine-readable memorystoring computer-executable instructions; and at least one hardwareprocessor in communication with the machine-readable memory that, whenthe computer-executable instructions are executed, configures thewearable computing device to perform a plurality of operations, theplurality of operations comprising: determining an environment type foran environment in which the wearable computing device is located, theenvironment type associated with an environment risk value; obtaining aplurality of biometric measurements from one or more biometric sensorscommunicatively coupled to the wearable computing device; classifyingthe plurality of biometric measurements into a plurality of categories,at least one category defining a characteristic of the obtainedbiometric measurements; determining a biometric input score based on theat least one category; comparing the determined biometric input scorewith the associated environment risk value; and in response to thecomparison of the determined biometric input score and the associatedenvironment risk value, identifying the plurality of biometricmeasurements as a false input to the wearable computing device.
 2. Thesystem of claim 1, wherein the plurality of operations further comprise:determining a readiness potential from the plurality of biometricmeasurements; comparing the determined readiness potential with apreviously measured baseline readiness potential; and in response to thecomparison of the determined readiness potential with the previouslymeasured baseline readiness potential, determining that the plurality ofbiometric measurements were from voluntary movements of a user of thewearable computing device.
 3. The system of claim 1, wherein theenvironment type is determined by cross-referencing Global PositioningSystem (GPS) coordinates with a location associated with the environmenttype.
 4. The system of claim 1, wherein determining the environment typecomprises: activating at least one external camera to detect a pluralityof objects; activating at least one audio sensor to detect one or moresounds; comparing the detected plurality of objects with at least oneobject threshold and the detected one or more sounds with at least onenoise threshold; and in response to the comparisons with the at leastone object threshold and the at least one noise threshold, determiningthe environment type.
 5. The system of claim 1, wherein each category ofthe plurality of categories is associated with a classification value,and the biometric input score is determined from the classificationvalues associated with the plurality of categories.
 6. The system ofclaim 1, wherein the plurality of operations further comprise:determining whether the plurality of biometric measurements were theresult of voluntary or involuntary movements by a user of the wearablecomputing device; and in response to a determination that the pluralityof biometric measurements were the result of voluntary movements,performing the classifying of the plurality biometric measurements intothe plurality of categories.
 7. The system of claim 1, wherein theplurality of operations further comprise: identifying the plurality ofbiometric measurements as an input to be interpreted as a commandperformable by the wearable computing device in response to thecomparison of the determined biometric input score and the associatedenvironment risk value.
 8. A method for providing augmented realityimages of an environment in which a wearable computing device is worn,the method comprising: determining, by at least one hardware processor,an environment type for an environment in which the wearable computingdevice is located, the environment type associated with an environmentrisk value; obtaining, by one or more biometric sensors communicativelycoupled to the wearable computing device, a plurality of biometricmeasurements; classifying the plurality of biometric measurements into aplurality of categories, at least one category defining a characteristicof the obtained biometric measurements; determining a biometric inputscore based on the at least one category; comparing the determinedbiometric input score with the associated environment risk value; and inresponse to the comparison of the determined biometric input score andthe associated environment risk value, identifying the plurality ofbiometric measurements as a false input to the wearable computingdevice.
 9. The method of claim 8, further comprising: determining areadiness potential from the plurality of biometric measurements;comparing the determined readiness potential with a previously measuredbaseline readiness potential; and in response to the comparison of thedetermined readiness potential with the previously measured baselinereadiness potential, determining that the plurality of biometricmeasurements were from voluntary movements of a user of the wearablecomputing device.
 10. The method of claim 8, wherein the environmenttype is determined by cross-referencing Global Positioning System (GPS)coordinates with a location associated with the environment type. 11.The method of claim 8, wherein determining the environment typecomprises: activating at least one external camera to detect a pluralityof objects; activating at least one audio sensor to detect one or moresounds; comparing the detected plurality of objects with at least oneobject threshold and the detected one or more sounds with at least onenoise threshold; and in response to the comparisons with the at leastone object threshold and the at least one noise threshold, determiningthe environment type.
 12. The method of claim 8, wherein each categoryof the plurality of categories is associated with a classificationvalue, and the biometric input score is determined from theclassification values associated with the plurality of categories. 13.The method of claim 8, further comprising: determining whether theplurality of biometric measurements were the result of voluntary orinvoluntary movements by a user of the wearable computing device; and inresponse to a determination that the plurality of biometric measurementswere the result of voluntary movements, performing the classifying ofthe plurality biometric measurements into the plurality of categories.14. The method of claim 8, further comprising: identifying the pluralityof biometric measurements as an input to be interpreted as a commandperformable by the wearable computing device in response to thecomparison of the determined biometric input score and the associatedenvironment risk value.
 15. A machine-readable memory storingcomputer-executable instructions that, when executed by at least onehardware processor in communication with the machine-readable memory,configures a wearable computing device to perform a plurality ofoperations, the plurality of operations comprising: determining anenvironment type for an environment in which the wearable computingdevice is located, the environment type associated with an environmentrisk value; obtaining a plurality of biometric measurements from one ormore biometric sensors communicatively coupled to the wearable computingdevice; classifying the plurality of biometric measurements into aplurality of categories, at least one category defining a characteristicof the obtained biometric measurements; determining a biometric inputscore based on the at least one category; comparing the determinedbiometric input score with the associated environment risk value; and inresponse to the comparison of the determined biometric input score andthe associated environment risk value, identifying the plurality ofbiometric measurements as a false input to the wearable computingdevice.
 16. The machine-readable memory of claim 15, wherein theplurality of operations further comprise: determining a readinesspotential from the plurality of biometric measurements; comparing thedetermined readiness potential with a previously measured baselinereadiness potential; and in response to the comparison of the determinedreadiness potential with the previously measured baseline readinesspotential, determining that the plurality of biometric measurements werefrom voluntary movements of a user of the wearable computing device. 17.The machine-readable memory of claim 15, wherein the environment type isdetermined by cross-referencing Global Positioning System (GPS)coordinates with a location associated with the environment type. 18.The machine-readable memory of claim 15, wherein determining theenvironment type comprises: activating at least one external camera todetect a plurality of objects; activating at least one audio sensor todetect one or more sounds; comparing the detected plurality of objectswith at least one object threshold and the detected one or more soundswith at least one noise threshold; and in response to the comparisonswith the at least one object threshold and the at least one noisethreshold, determining the environment type.
 19. The machine-readablememory of claim 15, wherein each category of the plurality of categoriesis associated with a classification value, and the biometric input scoreis determined from the classification values associated with theplurality of categories.
 20. The machine-readable memory of claim 15,wherein the plurality of operations further comprise: determiningwhether the plurality of biometric measurements were the result ofvoluntary or involuntary movements by a user of the wearable computingdevice; and in response to a determination that the plurality ofbiometric measurements were the result of voluntary movements,performing the classifying of the plurality biometric measurements intothe plurality of categories.